EQ5D: Descriptive analyses
Tory Madden and Peter Kamerman
25 October 2017
Import data
# Read in data
eq5d <- readr::read_rds('./data/eq5d.rds') %>%
arrange(ID) %>%
mutate(ID = stringr::str_to_lower(ID))
# For site and group info
foo <- readr::read_rds('./data/demographics.rds') %>%
select(ID, Site, Group, Sex) %>%
rename(ID2 = ID) %>%
arrange(ID2)
# Join the two datasets
eq5d <- foo %>%
bind_cols(eq5d) %>%
rowwise() %>%
mutate(ID_match = ifelse(ID2 == ID,
yes = TRUE,
no = FALSE))
# Check for mismatches from join
nrow(filter(eq5d, ID_match == FALSE))
# Eliminate extra ID column
eq5d <- eq5d %>%
select(-ID,
-ID_match) %>%
rename(ID = ID2)Quick look
glimpse(eq5d)## Observations: 160
## Variables: 40
## $ ID <chr> "j1", "j10", "j11", "j12", "j17", "j18", "j19",...
## $ Site <chr> "u1", "u1", "u1", "u1", "u1", "u1", "u1", "u1",...
## $ Group <chr> "p", "t", "p", "t", "t", "t", "t", "p", "t", "t...
## $ Sex <chr> "female", "male", "female", "female", "female",...
## $ Mobility_BL <dbl> 1, 2, 2, 1, 2, NA, 1, 2, 2, 2, NA, 1, 2, 1, 1, ...
## $ Mobility_Wk4 <dbl> NA, 1, 1, NA, NA, NA, 1, NA, 2, 1, NA, NA, NA, ...
## $ Mobility_Wk8 <dbl> NA, 2, 2, 1, NA, NA, 1, 2, 1, 1, NA, NA, NA, NA...
## $ Mobility_Wk12 <dbl> NA, 2, 2, 1, 2, NA, NA, NA, 2, 2, NA, NA, NA, N...
## $ Mobility_Wk24 <dbl> NA, 1, NA, 1, NA, NA, NA, 2, 1, 2, NA, NA, NA, ...
## $ Mobility_Wk48 <dbl> NA, 1, NA, 1, NA, NA, NA, 1, NA, 1, NA, NA, NA,...
## $ Self_care_BL <dbl> 2, 1, 1, 1, 1, NA, 1, 1, 1, 1, NA, 1, 1, 1, 1, ...
## $ Self_care_Wk4 <dbl> NA, 1, 1, NA, NA, NA, 1, NA, 1, 1, NA, NA, NA, ...
## $ Self_care_Wk8 <dbl> NA, 1, 1, 1, NA, NA, 1, 1, 1, 1, NA, NA, NA, NA...
## $ Self_care_Wk12 <dbl> NA, 1, 1, 1, 1, NA, NA, NA, 1, 1, NA, NA, NA, N...
## $ Self_care_Wk24 <dbl> NA, 1, NA, 1, NA, NA, NA, 2, 1, 1, NA, NA, NA, ...
## $ Self_care_Wk48 <dbl> NA, 1, NA, 1, NA, NA, NA, 1, NA, 1, NA, NA, NA,...
## $ Usual_act_BL <dbl> 2, 1, 2, 1, 2, NA, 1, 2, 2, 1, NA, 1, 3, 3, 2, ...
## $ Usual_act_Wk4 <dbl> NA, 1, 1, NA, NA, NA, 1, NA, 1, 1, NA, NA, NA, ...
## $ Usual_act_Wk8 <dbl> NA, 1, 3, 2, NA, NA, 1, 3, 1, 1, NA, NA, NA, NA...
## $ Usual_act_Wk12 <dbl> NA, 1, 3, 2, 3, NA, NA, NA, 1, 2, NA, NA, NA, N...
## $ Usual_act_Wk24 <dbl> NA, 1, NA, 1, NA, NA, NA, 3, 1, 3, NA, NA, NA, ...
## $ Usual_act_Wk48 <dbl> NA, 1, NA, 1, NA, NA, NA, 1, NA, 1, NA, NA, NA,...
## $ EQ_Pain_BL <dbl> NA, 3, 3, 1, 3, NA, 3, 3, 3, 2, NA, 3, 3, 3, 2,...
## $ EQ_Pain_Wk4 <dbl> NA, 3, 3, NA, NA, NA, 2, NA, 3, 3, NA, NA, NA, ...
## $ EQ_Pain_Wk8 <dbl> NA, 3, 3, 2, NA, NA, 2, 2, 2, 2, NA, NA, NA, NA...
## $ EQ_Pain_Wk12 <dbl> NA, 3, 3, 2, 3, NA, NA, NA, 2, 3, NA, NA, NA, N...
## $ EQ_Pain_Wk24 <dbl> NA, 3, NA, 2, NA, NA, NA, 3, 2, 3, NA, NA, NA, ...
## $ EQ_Pain_Wk48 <dbl> NA, 2, NA, 2, NA, NA, NA, 2, NA, 2, NA, NA, NA,...
## $ Anx_depr_BL <dbl> 2, 1, 1, 1, 1, NA, 1, 2, 1, 1, NA, 1, 1, 1, 1, ...
## $ Anx_depr_Wk4 <dbl> NA, 1, 1, NA, NA, NA, 1, NA, 1, 1, NA, NA, NA, ...
## $ Anx_depr_Wk8 <dbl> NA, 1, 1, 1, NA, NA, 1, 1, 1, 1, NA, NA, NA, NA...
## $ Anx_depr_Wk12 <dbl> NA, 1, 1, 1, 1, NA, NA, NA, 1, 1, NA, NA, NA, N...
## $ Anx_depr_Wk24 <dbl> NA, 1, NA, 1, NA, NA, NA, 1, 1, 1, NA, NA, NA, ...
## $ Anx_depr_Wk48 <dbl> NA, 1, NA, 1, NA, NA, NA, 1, NA, 1, NA, NA, NA,...
## $ SOH_BL <dbl> 60, 60, 30, 90, 80, NA, 80, 20, 70, 60, NA, 0, ...
## $ SOH_Wk4 <dbl> NA, 60, 80, NA, NA, NA, 80, NA, 70, 60, NA, NA,...
## $ SOH_Wk8 <dbl> NA, 70, 40, 80, NA, NA, 80, 50, 70, 80, NA, NA,...
## $ SOH_Wk12 <dbl> NA, 80, 20, 90, 0, NA, NA, NA, 70, 70, NA, NA, ...
## $ SOH_Wk24 <dbl> NA, 80, NA, 70, NA, NA, NA, 50, 70, 30, NA, NA,...
## $ SOH_Wk48 <dbl> NA, 60, NA, 70, NA, NA, NA, 80, NA, 90, NA, NA,...head(eq5d)tail(eq5d)EQ5D index score
Process data
# Calculate eq5d index score for each time point
# Create basic term = 1 for all cases in new column
eq5d$index_core <- 1
# Baseline #
############
# Pull out only BL data to new data frame for BL
BL_index <- select(eq5d, contains("BL"))
# Delete SOH column
BL_index <- BL_index %>%
select(-(contains("SOH")))
# Sum all rows for total BL index score and allocate to a new column in eq5d data frame
eq5d$BL_index_sum <- rowSums(BL_index)
# Create constant term to subtract if any domain scores > 1 (i.e. sum > 5)
eq5d <- eq5d %>% mutate(BL_constant_index = ifelse(BL_index_sum > 5, yes = 0.081, no = 0))
# Week 4 #
##########
# Pull out only Wk4 data to new data frame for Wk4
Wk4_index <- select(eq5d, contains("Wk4"))
# Delete SOH column
Wk4_index <- Wk4_index %>%
select(-(contains("SOH")))
# Sum all rows for total Wk4 index score and allocate to a new column in eq5d data frame
eq5d$Wk4_index_sum <- rowSums(Wk4_index)
# Create constant term to subtract if any domain scores > 1 (i.e. sum > 5)
eq5d <- eq5d %>% mutate(Wk4_constant_index = ifelse(Wk4_index_sum > 5,
yes = 0.081,
no = 0))
# Week 8 #
##########
# Pull out only Wk8 data to new data frame for Wk8
Wk8_index <- select(eq5d, contains("Wk8"))
# Delete SOH column
Wk8_index <- Wk8_index %>%
select(-(contains("SOH")))
# Sum all rows for total Wk8 index score and allocate to a new column in eq5d data frame
eq5d$Wk8_index_sum <- rowSums(Wk8_index)
# Create constant term to subtract if any domain scores > 1 (i.e. sum > 5)
eq5d <- eq5d %>% mutate(Wk8_constant_index = ifelse(Wk8_index_sum > 5,
yes = 0.081,
no = 0))
# Week 12 #
###########
# Pull out only Wk8 data to new data frame for Wk12
Wk12_index <- select(eq5d, contains("Wk12"))
# Delete SOH column
Wk12_index <- Wk12_index %>%
select(-(contains("SOH")))
# Sum all rows for total Wk12 index score and allocate to a new column in eq5d data frame
eq5d$Wk12_index_sum <- rowSums(Wk12_index)
# Create constant term to subtract if any domain scores > 1 (i.e. sum > 5)
eq5d <- eq5d %>% mutate(Wk12_constant_index = ifelse(Wk12_index_sum > 5,
yes = 0.081,
no = 0))
# Week 24 #
###########
# Pull out only Wk24 data to new data frame for Wk24
Wk24_index <- select(eq5d, contains("Wk24"))
# Delete SOH column
Wk24_index <- Wk24_index %>%
select(-(contains("SOH")))
# Sum all rows for total Wk24 index score and allocate to a new column in eq5d data frame
eq5d$Wk24_index_sum <- rowSums(Wk24_index)
# Create constant term to subtract if any domain scores > 1 (i.e. sum > 5)
eq5d <- eq5d %>% mutate(Wk24_constant_index = ifelse(Wk24_index_sum > 5,
yes = 0.081,
no = 0))
# Week 48 #
###########
# Pull out only Wk48 data to new data frame for Wk48
Wk48_index <- select(eq5d, contains("Wk48"))
# Delete SOH column
Wk48_index <- Wk48_index %>%
select(-(contains("SOH")))
# Sum all rows for total Wk48 index score and allocate to a new column in eq5d data frame
eq5d$Wk48_index_sum <- rowSums(Wk48_index)
# Create constant term to subtract if any domain scores > 1 (i.e. sum > 5)
eq5d <- eq5d %>% mutate(Wk48_constant_index = ifelse(Wk48_index_sum > 5,
yes = 0.081,
no = 0))
# 1. Create variable for subtraction for each domain and time point:
# Call it (for example) BL_Mobility_index
# Use values from table in an ifelse statement
# THEN
# 2. Compute the index score for each time point using:
# BL_index_score = index_core - constant_index - BL_Mobility_index - (etc - for each domain).
# Baseline #
############
eq5d <- eq5d %>% mutate(BL_Mobility_index = ifelse(Mobility_BL == 2,
yes = 0.069,
no = ifelse(Mobility_BL == 3,
yes = 0.314,
no = 0)))
eq5d <- eq5d %>% mutate(BL_Self_care_index = ifelse(Self_care_BL == 2,
yes = 0.104,
no = ifelse(Self_care_BL == 3,
yes = 0.214,
no = 0)))
eq5d <- eq5d %>% mutate(BL_Usual_act_index = ifelse(Usual_act_BL == 2,
yes = 0.036,
no = ifelse(Usual_act_BL == 3,
yes = 0.094,
no = 0)))
eq5d <- eq5d %>% mutate(BL_EQ_Pain_index = ifelse(EQ_Pain_BL == 2,
yes = 0.123,
no = ifelse(EQ_Pain_BL == 3,
yes = 0.386,
no = 0)))
eq5d <- eq5d %>% mutate(BL_Anx_depr_index = ifelse(Anx_depr_BL == 2,
yes = 0.071,
no = ifelse(Anx_depr_BL == 3,
yes = 0.236,
no = 0)))
# Index score
eq5d <- eq5d %>% mutate(BL_index_score = index_core - BL_constant_index - BL_Mobility_index - BL_Self_care_index - BL_Usual_act_index - BL_EQ_Pain_index - BL_Anx_depr_index)
# Week 4 #
##########
eq5d <- eq5d %>% mutate(Wk4_Mobility_index = ifelse(Mobility_Wk4 == 2,
yes = 0.069,
no = ifelse(Mobility_Wk4 == 3,
yes = 0.314,
no = 0)))
eq5d <- eq5d %>% mutate(Wk4_Self_care_index = ifelse(Self_care_Wk4 == 2,
yes = 0.104,
no = ifelse(Self_care_Wk4 == 3,
yes = 0.214,
no = 0)))
eq5d <- eq5d %>% mutate(Wk4_Usual_act_index = ifelse(Usual_act_Wk4 == 2,
yes = 0.036,
no = ifelse(Usual_act_Wk4 == 3,
yes = 0.094,
no = 0)))
eq5d <- eq5d %>% mutate(Wk4_EQ_Pain_index = ifelse(EQ_Pain_Wk4 == 2,
yes = 0.123,
no = ifelse(EQ_Pain_Wk4 == 3,
yes = 0.386,
no = 0)))
eq5d <- eq5d %>% mutate(Wk4_Anx_depr_index = ifelse(Anx_depr_Wk4 == 2,
yes = 0.071,
no = ifelse(Anx_depr_Wk4 == 3,
yes = 0.236,
no = 0)))
# Index score
eq5d <- eq5d %>% mutate(Wk4_index_score = index_core - Wk4_constant_index - Wk4_Mobility_index - Wk4_Self_care_index - Wk4_Usual_act_index - Wk4_EQ_Pain_index - Wk4_Anx_depr_index)
# Week 8 #
##########
eq5d <- eq5d %>% mutate(Wk8_Mobility_index = ifelse(Mobility_Wk8 == 2,
yes = 0.069,
no = ifelse(Mobility_Wk8 == 3,
yes = 0.314,
no = 0)))
eq5d <- eq5d %>% mutate(Wk8_Self_care_index = ifelse(Self_care_Wk8 == 2,
yes = 0.104,
no = ifelse(Self_care_Wk8 == 3,
yes = 0.214,
no = 0)))
eq5d <- eq5d %>% mutate(Wk8_Usual_act_index = ifelse(Usual_act_Wk8 == 2,
yes = 0.036,
no = ifelse(Usual_act_Wk8 == 3,
yes = 0.094,
no = 0)))
eq5d <- eq5d %>% mutate(Wk8_EQ_Pain_index = ifelse(EQ_Pain_Wk8 == 2,
yes = 0.123,
no = ifelse(EQ_Pain_Wk8 == 3,
yes = 0.386,
no = 0)))
eq5d <- eq5d %>% mutate(Wk8_Anx_depr_index = ifelse(Anx_depr_Wk8 == 2,
yes = 0.071,
no = ifelse(Anx_depr_Wk8 == 3,
yes = 0.236,
no = 0)))
# Index score
eq5d <- eq5d %>% mutate(Wk8_index_score = index_core - Wk8_constant_index - Wk8_Mobility_index - Wk8_Self_care_index - Wk8_Usual_act_index - Wk8_EQ_Pain_index - Wk8_Anx_depr_index)
# Week 12 #
###########
eq5d <- eq5d %>% mutate(Wk12_Mobility_index = ifelse(Mobility_Wk12 == 2,
yes = 0.069,
no = ifelse(Mobility_Wk12 == 3,
yes = 0.314,
no = 0)))
eq5d <- eq5d %>% mutate(Wk12_Self_care_index = ifelse(Self_care_Wk12 == 2,
yes = 0.104,
no = ifelse(Self_care_Wk12 == 3,
yes = 0.214,
no = 0)))
eq5d <- eq5d %>% mutate(Wk12_Usual_act_index = ifelse(Usual_act_Wk12 == 2,
yes = 0.036,
no = ifelse(Usual_act_Wk12 == 3,
yes = 0.094,
no = 0)))
eq5d <- eq5d %>% mutate(Wk12_EQ_Pain_index = ifelse(EQ_Pain_Wk12 == 2,
yes = 0.123,
no = ifelse(EQ_Pain_Wk12 == 3,
yes = 0.386,
no = 0)))
eq5d <- eq5d %>% mutate(Wk12_Anx_depr_index = ifelse(Anx_depr_Wk12 == 2,
yes = 0.071,
no = ifelse(Anx_depr_Wk12 == 3,
yes = 0.236,
no = 0)))
# Index score
eq5d <- eq5d %>% mutate(Wk12_index_score = index_core - Wk12_constant_index - Wk12_Mobility_index - Wk12_Self_care_index - Wk12_Usual_act_index - Wk12_EQ_Pain_index - Wk12_Anx_depr_index)
# Week 24 #
###########
eq5d <- eq5d %>% mutate(Wk24_Mobility_index = ifelse(Mobility_Wk24 == 2,
yes = 0.069,
no = ifelse(Mobility_Wk24 == 3,
yes = 0.314,
no = 0)))
eq5d <- eq5d %>% mutate(Wk24_Self_care_index = ifelse(Self_care_Wk24 == 2,
yes = 0.104,
no = ifelse(Self_care_Wk24 == 3,
yes = 0.214,
no = 0)))
eq5d <- eq5d %>% mutate(Wk24_Usual_act_index = ifelse(Usual_act_Wk24 == 2,
yes = 0.036,
no = ifelse(Usual_act_Wk24 == 3,
yes = 0.094,
no = 0)))
eq5d <- eq5d %>% mutate(Wk24_EQ_Pain_index = ifelse(EQ_Pain_Wk24 == 2,
yes = 0.123,
no = ifelse(EQ_Pain_Wk24 == 3,
yes = 0.386,
no = 0)))
eq5d <- eq5d %>% mutate(Wk24_Anx_depr_index = ifelse(Anx_depr_Wk24 == 2,
yes = 0.071,
no = ifelse(Anx_depr_Wk24 == 3,
yes = 0.236,
no = 0)))
# Index score
eq5d <- eq5d %>% mutate(Wk24_index_score = index_core - Wk24_constant_index - Wk24_Mobility_index - Wk24_Self_care_index - Wk24_Usual_act_index - Wk24_EQ_Pain_index - Wk24_Anx_depr_index)
# Week 48 #
###########
eq5d <- eq5d %>% mutate(Wk48_Mobility_index = ifelse(Mobility_Wk48 == 2,
yes = 0.069,
no = ifelse(Mobility_Wk48 == 3,
yes = 0.314,
no = 0)))
eq5d <- eq5d %>% mutate(Wk48_Self_care_index = ifelse(Self_care_Wk48 == 2,
yes = 0.104,
no = ifelse(Self_care_Wk48 == 3,
yes = 0.214,
no = 0)))
eq5d <- eq5d %>% mutate(Wk48_Usual_act_index = ifelse(Usual_act_Wk48 == 2,
yes = 0.036,
no = ifelse(Usual_act_Wk48 == 3,
yes = 0.094,
no = 0)))
eq5d <- eq5d %>% mutate(Wk48_EQ_Pain_index = ifelse(EQ_Pain_Wk48 == 2,
yes = 0.123,
no = ifelse(EQ_Pain_Wk48 == 3,
yes = 0.386,
no = 0)))
eq5d <- eq5d %>% mutate(Wk48_Anx_depr_index = ifelse(Anx_depr_Wk48 == 2,
yes = 0.071,
no = ifelse(Anx_depr_Wk48 == 3,
yes = 0.236,
no = 0)))
# Index score
eq5d <- eq5d %>% mutate(Wk48_index_score = index_core - Wk48_constant_index - Wk48_Mobility_index - Wk48_Self_care_index - Wk48_Usual_act_index - Wk48_EQ_Pain_index - Wk48_Anx_depr_index)
# Check
glimpse(eq5d)
# Create smaller data frame with index data only
eq5d_index <- eq5d %>%
select(ID, Site, Group, Sex, contains("index_score"))
# Gather from wide format into long format
eq5d_index <- eq5d_index %>%
tidyr::gather(key = Week,
value = Score,
-ID, - Site, - Group, -Sex)
# Separate column labels to convert to numbers
# Separate at underscore, delete Text column, convert to character
eq5d_index <- tidyr::separate(data = eq5d_index,
col = Week,
into = c('Week', 'Text'))
eq5d_index <- eq5d_index %>% select(-Text)
eq5d_index <- mutate(eq5d_index,
Week = as.character(Week))
# Replace values
eq5d_index[eq5d_index=="BL"] <- "BL0"
# Separate after 2 characters
eq5d_index <- tidyr::separate(data = eq5d_index,
col = Week,
into = c('Text', 'Week'), 2)
head(eq5d_index)
# Convert back into numeric form and delete Text column
eq5d_index <- mutate(eq5d_index,
Week = as.numeric(Week))
eq5d_index <- eq5d_index %>% select(-Text)
# Check data
glimpse(eq5d_index)Plots
Spaghetti plots
ggplot(data = eq5d_index) +
aes(x = Week,
y = Score,
group = ID) %>%
geom_line() +
labs(title = 'EQ5D index score',
subtitle = 'All participants, all sites')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index) +
aes(x = Week,
y = Score,
colour = Site,
group = ID) %>%
geom_line() +
labs(title = 'EQ5D index score',
subtitle = 'Coloured by Site')+
theme(legend.position = 'right') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line() +
facet_grid(Site ~ .) +
labs(title = 'EQ5D index score',
subtitle = 'Faceted by study site, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line() +
facet_grid(Site ~ Sex) +
labs(title = 'EQ5D index score',
subtitle = 'Faceted by study site and gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
# Separate sites
eq5d_index_J <- eq5d_index %>% filter(str_detect(Site, "u1"))
eq5d_index_M <- eq5d_index %>% filter(str_detect(Site, "r1"))
eq5d_index_R <- eq5d_index %>% filter(str_detect(Site, "u2"))
eq5d_index_Z <- eq5d_index %>% filter(str_detect(Site, "r2"))
ggplot(data = eq5d_index_J) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line() +
facet_grid(Sex ~ .) +
labs(title = 'Site J: EQ5D index score',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_J) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line(size = 0.3,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Sex ~ .) +
labs(title = 'Site J: EQ5D index score WITH jitter added',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_J) +
aes(x = Week,
y = Score,
group = ID,
colour = Group) %>%
geom_line() +
geom_point(aes(x = Week,
y = Score,
colour = Group)) +
labs(title = 'Site J: EQ5D index score',
subtitle = 'Faceted by gender, coloured by Group')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_M) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line() +
facet_grid(Sex ~ .) +
labs(title = 'Site M: EQ5D index score',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_M) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line(size = 0.3,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Sex ~ .) +
labs(title = 'Site M: EQ5D index score WITH jitter added',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_R) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line() +
facet_grid(Sex ~ .) +
labs(title = 'Site R: EQ5D index score',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_R) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line(size = 0.3,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Sex ~ .) +
labs(title = 'Site R: EQ5D index score WITH jitter added',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_Z) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line(size = 0.3,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Sex ~ .) +
labs(title = 'Site Z: EQ5D index score',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
ggplot(data = eq5d_index_Z) +
aes(x = Week,
y = Score,
colour = ID) %>%
geom_line(size = 0.3,
position = position_jitterdodge(jitter.height = 0.02, dodge.width = 0.2)) +
facet_grid(Sex ~ .) +
labs(title = 'Site Z: EQ5D index score WITH jitter added',
subtitle = 'Faceted by gender, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(-0.6,-0.3,0,0.3,0.6,0.9,1))
Summary plots
# By group
ggplot(data = eq5d_index) +
aes(x = as.factor(Week), # Reclassify 'Week' from numeric to factor
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'EQ5D State of Health',
subtitle = 'Coloured by group')
ggplot(data = eq5d_index) +
aes(x = as.factor(Week),
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
facet_grid(Site ~ .) +
labs(title = 'EQ5D State of Health',
subtitle = 'Faceted by study site, coloured by group')
# Collapse across groups and look at sites:
ggplot(data = eq5d_index) +
aes(x = as.factor(Week),
y = Score,
colour = Site,
fill = Site) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'EQ5D State of Health',
subtitle = 'All groups, coloured by study site')
eq5d_index %>%
filter(Site == "u1") %>%
mutate(Week = as.factor(Week)) %>% # Reclassify 'Week' from numeric to factor
ggplot(.) +
aes(x = Week,
y = Score,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'EQ5D State of Health',
subtitle = 'Site J, coloured by gender')
eq5d_index %>%
filter(Site == "u1") %>%
mutate(Week = as.factor(Week)) %>% # Reclassify 'Week' from numeric to factor
ggplot(.) +
aes(x = Week,
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
facet_grid(Sex ~ .) +
labs(title = 'EQ5D State of Health',
subtitle = 'Site J, coloured by group, faceted by gender')
# By site:
eq5d_index %>%
filter(Site == "u1") %>%
mutate(Week = as.factor(Week)) %>% # Reclassify 'Week' from numeric to factor
ggplot(.) +
aes(x = Week,
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'Site J: EQ5D State of Health',
subtitle = 'Coloured by group')
eq5d_index %>%
filter(Site == "r1") %>%
mutate(Week = as.factor(Week)) %>% # Reclassify 'Week' from numeric to factor
ggplot(.) +
aes(x = Week,
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'Site M: EQ5D State of Health',
subtitle = 'Coloured by group')
eq5d_index %>%
filter(Site == "u2") %>%
mutate(Week = as.factor(Week)) %>% # Reclassify 'Week' from numeric to factor
ggplot(.) +
aes(x = Week,
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'Site R: EQ5D State of Health',
subtitle = 'Coloured by group')
eq5d_index %>%
filter(Site == "r2") %>%
mutate(Week = as.factor(Week)) %>% # Reclassify 'Week' from numeric to factor
ggplot(.) +
aes(x = Week,
y = Score,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
scale_colour_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1") +
labs(title = 'Site Z: EQ5D State of Health',
subtitle = 'Coloured by group')
EQ5D state of health
(Not too much on it because it is misunderstood)
# Create plots for EQ5D VAS: State Of Health scale
eq5d_SOH <- select(eq5d,ID, Site, Group, Sex, starts_with('SOH'))
# Gather from wide format into long format
eq5d_SOH <- eq5d_SOH %>%
tidyr::gather(key = time_point,
value = VAS_rating,
-ID, - Site, - Group, -Sex)
# Create new column with time
eq5d_SOH <- eq5d_SOH %>%
mutate(Week = case_when(
stringr::str_detect(.$time_point, "Wk48") ~ 48,
stringr::str_detect(.$time_point, "BL") ~ 0,
stringr::str_detect(.$time_point, "Wk4$") ~ 4,
stringr::str_detect(.$time_point, "Wk8") ~ 8,
stringr::str_detect(.$time_point, "Wk12") ~ 12,
stringr::str_detect(.$time_point, "Wk24") ~ 24
))
# Delete time_point column
eq5d_SOH <- eq5d_SOH %>% select(-time_point)Plots
Spaghetti plots
ggplot(data = eq5d_SOH) +
aes(x = Week,
y = VAS_rating,
colour = ID,
group = ID
) %>%
geom_line(size = 0.2,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D State of Health',
subtitle = 'Faceted by study site, coloured by ID')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,20,40,60,100))
ggplot(data = eq5d_SOH,
aes(x = Week,
y = VAS_rating,
group = ID,
colour = Group)) +
geom_line(size = 0.5,
position = position_jitterdodge(dodge.width = 0.2)) +
geom_point(aes(shape = Group),
size = 1) +
scale_colour_brewer(palette = "Set1") +
scale_shape_manual(values = c(22,24,21)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D State of Health',
subtitle = 'Faceted by study site, coloured by group')+
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(0,20,40,60,80,100))
Summary plots
ggplot(data = eq5d_SOH) +
aes(x = as.factor(Week),
y = VAS_rating,
colour = Group,
fill = Group) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D State of Health',
subtitle = 'Faceted by study site, coloured by group')
ggplot(data = eq5d_SOH) +
aes(x = as.factor(Week),
y = VAS_rating,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Group ~ .) +
labs(title = 'EQ5D State of Health',
subtitle = 'Faceted by study group, coloured by sex')
ggplot(data = eq5d_SOH) +
aes(x = as.factor(Week),
y = VAS_rating,
colour = Sex,
fill = Sex) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ Group) +
labs(title = 'EQ5D State of Health',
subtitle = 'Faceted by study site and group,\ncoloured by sex')
ggplot(data = filter(eq5d_SOH, Sex == 'female')) +
aes(x = as.factor(Week),
y = VAS_rating) %>%
geom_boxplot(alpha = 0.6) +
labs(title = 'EQ5D State of Health',
subtitle = 'Women only')
ggplot(data = filter(eq5d_SOH, Sex == 'female' & Site != 'u1')) +
aes(x = as.factor(Week),
y = VAS_rating) %>%
geom_boxplot(alpha = 0.6) +
labs(title = 'EQ5D State of Health',
subtitle = 'Women only (excluding U1)')
EQ5D domains
# Remove data for index and SOH
eq5d_domains <- eq5d[, -grep("index", colnames(eq5d))]
eq5d_domains <- eq5d_domains[, -grep("SOH", colnames(eq5d))]
# Gather from wide format into long format
eq5d_tot <- eq5d_domains %>%
tidyr::gather(key = eq5d_question,
value = eq5d_rating,
-ID, - Site, - Group, -Sex)
# Create new column with time
eq5d_tot <- eq5d_tot %>%
mutate(Week = case_when(
stringr::str_detect(.$eq5d_question, "Wk48") ~ 48,
stringr::str_detect(.$eq5d_question, "BL") ~ 0,
stringr::str_detect(.$eq5d_question, "Wk4$") ~ 4,
stringr::str_detect(.$eq5d_question, "Wk8") ~ 8,
stringr::str_detect(.$eq5d_question, "Wk12") ~ 12,
stringr::str_detect(.$eq5d_question, "Wk24") ~ 24
))
# Create new column with domain
eq5d_tot <- eq5d_tot %>%
mutate(Domain = case_when(
stringr::str_detect(.$eq5d_question, "Mobility") ~ "Mobility",
stringr::str_detect(.$eq5d_question, "Self") ~ "Self-care",
stringr::str_detect(.$eq5d_question, "Usual") ~ "Usual activities",
stringr::str_detect(.$eq5d_question, "Pain") ~ "Pain",
stringr::str_detect(.$eq5d_question, "Anx") ~ "Anxiety-depression"
))
# Make Week numeric
eq5d_tot %>% mutate(Week = as.numeric(Week))# Make Group categorical
eq5d_tot %>% mutate(Group = as.numeric(Group))Plots
Spaghetti plots
ggplot(data = eq5d_tot) +
aes(x = Week,
y = eq5d_rating,
group = ID,
colour = Domain) %>%
geom_line(size = 0.2,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Site ~ Domain) +
labs(title = 'EQ5D Domains',
subtitle = 'Faceted by study site and domain, coloured by domain') +
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48))
# Consider Mobility only
eq5d_mobility <- select(eq5d_domains, ID, Site, Group, Sex, contains("Mobility"))
eq5d_mobility <- eq5d_mobility %>%
tidyr::gather(key = eq5d_question,
value = eq5d_rating,
-ID, - Site, - Group, -Sex)
# Create new column with time
eq5d_mobility <- eq5d_mobility %>%
mutate(Week = case_when(
stringr::str_detect(.$eq5d_question, "Wk48") ~ 48,
stringr::str_detect(.$eq5d_question, "BL") ~ 0,
stringr::str_detect(.$eq5d_question, "Wk4$") ~ 4,
stringr::str_detect(.$eq5d_question, "Wk8") ~ 8,
stringr::str_detect(.$eq5d_question, "Wk12") ~ 12,
stringr::str_detect(.$eq5d_question, "Wk24") ~ 24
))
ggplot(data = eq5d_mobility) +
aes(x = Week,
y = eq5d_rating,
group = ID,
colour = ID) %>%
geom_line(size = 0.2,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D Mobility',
subtitle = 'Faceted by study site, coloured by ID') +
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(3,2,1))
ggplot(data = filter(eq5d_tot, Domain == 'Self-care')) +
aes(x = Week,
y = eq5d_rating,
group = ID,
colour = ID) %>%
geom_line(size = 0.2,
position = position_jitterdodge(jitter.height = 0.1, dodge.width = 0.2)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D Self-care',
subtitle = 'Faceted by study site, coloured by ID') +
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48)) +
scale_y_continuous(breaks = c(3,2,1))
ggplot(data = filter(eq5d_tot, Domain == 'Usual activities')) +
aes(x = Week,
y = eq5d_rating,
group = ID,
colour = ID) %>%
geom_line(size = 0.2,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D Usual activities',
subtitle = 'Faceted by study site, coloured by ID') +
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48))
eq5d_tot %>%
filter(Domain == "Pain") %>%
ggplot(.) +
aes(x = Week,
y = eq5d_rating,
group = ID,
colour = ID) %>%
geom_line(size = 0.2,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D Pain',
subtitle = 'Faceted by study site, coloured by ID') +
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48))
eq5d_tot %>%
filter(Domain == "Anxiety-depression") %>%
ggplot(.) +
aes(x = Week,
y = eq5d_rating,
group = ID,
colour = ID) %>%
geom_line(size = 0.2,
position = position_jitterdodge(dodge.width = 0.2)) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D Anxiety-depression',
subtitle = 'Faceted by study site, coloured by ID') +
theme(legend.position = 'none') +
scale_x_continuous(breaks = c(0,4,8,12,24,48),
limits = c(0, 48))
Summary plots
ggplot(data = eq5d_tot) +
aes(x = as.factor(Week),
y = eq5d_rating,
colour = Domain,
fill = Domain) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ .) +
labs(title = 'EQ5D Domains',
subtitle = 'Faceted by study site, coloured by domain')
ggplot(data = eq5d_tot) +
aes(x = as.factor(Week),
y = eq5d_rating,
colour = Domain,
fill = Domain) %>%
geom_boxplot(alpha = 0.6) +
facet_grid(Site ~ Domain) +
labs(title = 'EQ5D Domains',
subtitle = 'Faceted by study site and domain')
Session information
sessionInfo()## R version 3.4.2 (2017-09-28)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2 viridis_0.4.0 viridisLite_0.2.0 stringr_1.2.0
## [5] ggplot2_2.2.1 dplyr_0.7.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.13 RColorBrewer_1.1-2 compiler_3.4.2
## [4] plyr_1.8.4 bindr_0.1 tools_3.4.2
## [7] digest_0.6.12 jsonlite_1.5 evaluate_0.10.1
## [10] tibble_1.3.4 gtable_0.2.0 pkgconfig_2.0.1
## [13] rlang_0.1.2 yaml_2.1.14 gridExtra_2.3
## [16] knitr_1.17 hms_0.3 rprojroot_1.2
## [19] grid_3.4.2 tidyselect_0.2.2 glue_1.1.1
## [22] R6_2.2.2 rmarkdown_1.6 readr_1.1.1
## [25] tidyr_0.7.2 purrr_0.2.4 reshape2_1.4.2
## [28] magrittr_1.5 backports_1.1.1 scales_0.5.0
## [31] htmltools_0.3.6 assertthat_0.2.0 colorspace_1.3-2
## [34] labeling_0.3 stringi_1.1.5 lazyeval_0.2.0
## [37] munsell_0.4.3